Bio:Kalyanis a Research Scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL,MIT). His primary research interests are in machine learning and building large scale statistical models that enable discovery from large amounts of data. His research is at the intersection of Big data, machine learning and data science. He co-leads a group called Any Scale learning for all. The group is interested in Big data science and Machine learning, and is comprised of 20 members: postdoctoral fellows, graduate (MEng, S.M., and Ph.D), and undergraduate students.

Current research:During the past three years I have set out to answer a seemingly simple question: “why does it take so much time to process, analyze and derive insights from data?”. I ventured into a number of domains (Education, Medicine, and Energy) and designed several novel approaches. This has allowed me to identify critical issues at the very foundation of the way we interact with, work around barriers and materialize insights from data. Consequently, I have founded multiple long term projects with a vision of making human interaction with data easier. In addition to simply scaling machine learning approaches, novel approaches, systems were required. These novel methods include scaling of processes that have “human-in-the-loop”, identification and storage of intermediary pre-processed data structures for re-use, and the creation of interfaces to exploit such intermediate structures. Ultimately, this has led me to design approaches and methods for automating much of the data science endeavor.